Abstract
To understand how factors influence ρDCCA, we analyzed cross-correlation between time series, including artificial time series generated by the autoregressive fractionally integrated moving average method and monthly precipitation record series in nature, with various scaling exponents. The results show that the contribution of ρDCCA primarily comes from scaling exponents and input excitation sources. Precipitation behavior is complicated in the real world, and the scaling exponent of precipitation series, estimated by using the detrended fluctuation analysis method, is closely relevant to the length and the starting time of the precipitation record series as well as the scaling range. ρDCCA between weather stations is related to the chosen precipitation series which possibly contain dynamic processes. In comparing ρDCCA between original monthly precipitation record series to ρDCCA between input excitation sources, the cross-correlation between input excitation sources can reflect intrinsic characteristic. There are more spatial cross-correlation patterns over eastern China on various time scales.
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